arXiv Notify me• Mar 31, 2022paper

Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio}$

Noam Shazeer(guest)

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Executive Summary

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The Engineering Challenges of Scaling AI Modular and Specialized Tooling for MLOps

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Concerns Raised

Complexity of distributing computation on supercomputer clusters.
Potential for data infeeding bottlenecks during large-scale training.
Difficulty in ensuring reproducible results in complex AI experiments.

Opportunities Identified

Simplified process for building and training large language models at scale.
Creation of fast, reusable, and reproducible data and evaluation pipelines.
Increased access to state-of-the-art training infrastructure for the broader research community.

Key Themes

Research Findings6

The t5x and seqio libraries have been used to train models with hundreds of billions of parameters on datasets with multiple terabytes of training data.

The t5x software library simplifies the process of building and training large language models at scale while maintaining ease of use.

The seqio software library provides a task-based API for creating fast and reproducible training data and evaluation pipelines.

The release of t5x and seqio includes configurations and instructions for T5-like encoder-decoder models and GPT-like decoder-only architectures.

The t5x library is open source and available on GitHub at google-research/t5x.

The seqio library is open source and available on GitHub at google/seqio.

Topics

Large Language Models (LLMs)Model Scaling MLOps AI Infrastructure Distributed Computing TPU Data Pipelines Reproducibility Open Source AI t5x seqio Google Research Encoder-Decoder Models Decoder-Only Models

Processed Jul 28, 2026Daily intelligence brief → yt-dlp + mlx-whisper + Gemini

Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio}$

Continue your research

Concerns Raised

Opportunities Identified

Key Themes

The Engineering Challenges of Scaling AI

Modular and Specialized Tooling for MLOps

Democratization through Open Source Infrastructure

Prioritizing Reproducibility in AI Research

Research Findings6

Topics